Intelligent reflecting surface (IRS) is emerging as a transformative technology for next-generation wireless communication and sensing systems. In this letter, we consider the problem of adaptive beamforming (ABF) for a single-antenna receiver aided by a nearby IRS, where the receiver aims to extract signals from a desired direction in the presence of$K$strong,unknowninterferences. Specifically, we propose to maximize the signal-to-interference-plus-noise ratio (SINR) by optimizing the reflection coefficients of the IRS. Unlike conventional ABF methods, we do not have direct access to the signal-plus-interference covariance matrix. Instead, only a limited number of quadratic compressive measurements can be obtained. To close this gap, we present a sample-efficient analytical solution via implicit inference of the interference covariance matrix. Simulation results demonstrate that our method significantly improves the SINR over state-of-the-art approaches.

We investigate wideband mmWave massive multiple-input multiple-output (MIMO) beamforming for near-field/far-field integrated sensing, communication and computation over-the-air (ISCCO) systems with multi-antenna receivers, a scenario that has not been addressed in existing works focusing on single-antenna receivers for near-field beamforming. The data from integrated sensing and communication devices is transmitted to a multi-antenna access point for data fusion by utilizing over-the-air computation, which improves spectral efficiency and reduces overhead through the addition of analog waves. We formulate the near-field/far-field wideband mmWave massive MIMO beamforming problem by maximizing the computational mean square error performance over subcarriers while guaranteeing the sensing performance measured by Cram´er-Rao bound subject to the power constraint. We propose two approaches for solving this problem. The first approach provides a fully-digital scheme serving as a performance benchmark by using the alternating direction method of multipliers algorithm. The second approach aims to further reduce computational complexity by multibeam beamforming with respect to the carefully designed analog beamformer based on the approximated channel. Simulation results demonstrate the effectiveness and low complexity of our proposed multibeam beamformer, applicable to near-field/far-field wideband mmWave ISCCO systems.

Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates in next-generation communications. A large array aperture, along with the ever increasing carrier frequency over the mmWave/THz bands, leads to a large Rayleigh distance. As a result, the traditional planar-wave assumption may not hold valid for mmWave/THz systems featuring ELAAs. In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account. By analyzing the coherence properties of any two near-field steering vectors, we prove that the hybrid near/far-field channel admits a block-sparse representation on a specially designed unitary matrix. Specifically, the percentage of nonzero elements of such a block-sparse representation is in the order of 1/√N, which tends to zero as the number of antennas,N, grows. Such a block-sparse representation allows to convert channel estimation into a block-sparse signal recovery problem. Simulation results are provided to verify our theoretical results and illustrate the performance of the proposed channel estimation approach in comparison with existing state-of-the-art methods.

This paper considers channel estimation for extremely large-scale intelligent reflecting surface (XL-IRS)-assisted terahertz (THz) communication systems. Specifically, an XL-IRS is deployed close to users (UEs) to enhance communication performance between the base station (BS) and UE. With its large aperture, the XL-IRS has a Rayleigh distance of tens of meters. Therefore, the users are likely located in the near-field region of the XL-IRS, while the BS is in its far-field region. Consequently, a spherical wavefront propagation model should be considered to characterize the propagation property between the XL-IRS and the UE, while the planar wavefront propagation model is utilized in the BS-IRS link. By leveraging Khatri-Rao product and Kronecker product properties, we rephrase the channel estimation problem. In addition, we construct an orthogonal dictionary, which essentially modifies the well-known Discrete Fourier Transform (DFT) matrix. We further find that the considered channel can be block-sparsely represented by this dictionary. Hence, the channel estimation can be converted into a block sparse recovery problem, which can be efficiently solved by several off-the-shelf methods. The simulation results show that our proposed method achieves better estimation performance than the conventional polar-domain-based method.

Target-mounted intelligent reflecting surfaces (IRS) introduce a novel degree of freedom (DoF) in controlling the target’s radar cross section (RCS), thereby enabling numerous advanced applications in wireless sensing and integrated sensing and communication (ISAC) systems. Nevertheless, a comprehensive analytical framework characterizing the impact of IRS reflection coefficients on wireless sensing performance remains largely unexplored in existing literature. To address this gap, this paper investigates sensing mutual information (SMI) in a general scenario where a sensing transmitter (TX) sends random signals to multiple targets each equipped with an IRS, and multiple sensing receivers (RXs) process the received echoes. We derive a closed-form tight upper bound on SMI and propose an efficient manifold optimization-based method to maximize it by jointly optimizing the transmit precoder and IRS reflection coefficients. Simulation results validate our analysis and demonstrate substantial enhancements in SMI achieved by the proposed method.

We study the problem of interference cancelation with the aid of an intelligent reflecting surface (IRS), where the objective is to determine the reflection coefficients at the IRS such that the interference signals are canceled at the receiver. Specifically, we are interested in a “blind” scenario where the channel state information (CSI) between the interference sources and the receiver is unknown. To tackle this challenging problem, we propose a sample-efficient blind approach which utilizes a small number of average received signal power measurements to automatically identify a reflection coefficient vector that is orthogonal to the cascaded interference channels and thus nullifies the interference signals at the receiver. Simulation results show that the proposed method can effectively cancel the interference signals and enhance the signal-to-interference-plus-noise ratio (SINR).

In this article, we consider the problem of joint transceiver design for millimeter-wave (mmWave)/terahertz (THz) multiuser MIMO integrated sensing and communication (ISAC) systems. Such a problem is formulated into a nonconvex optimization problem, with the objective of maximizing a weighted sum of communication users’ rates and the passive radar’s signal-to-clutter-and-noise ratio (SCNR). By exploring a low-dimensional subspace property of the optimal precoder, a low-dimensional subspace property-inspired block-coordinate-descent (LS-BCD)-based algorithm is proposed with remarkably reduced computational complexity. Our analysis reveals that the hybrid analog/digital beamforming structure can attain the same performance as that of a fully digital precoder, provided that the number of radio frequency (RF) chains is no less than the number of resolvable signal paths. Also, through expressing the precoder as a sum of a communication-precoder and a sensing-precoder, we develop an analytical solution to the joint transceiver design problem by generalizing the idea of block diagonalization (BD) to the ISAC system. Simulation results show that with a proper tradeoff parameter, the proposed methods can achieve a decent compromise between communication and sensing, where the performance of each communication/sensing task experiences only a mild performance loss as compared with the performance attained by optimizing exclusively for a single task.

In this paper, we propose an analytical solution for recovering a low-rank positive semi-definite (PSD) matrix from its rank-one measurements. We show that by utilizing a set of structured measurement vectors, we can analytically determine the null space of this low-rank PSD matrix. Based on the result, the PSD matrix can be efficiently recovered. Our analysis shows that the proposed method only requires$(N-K)(2K+1) + K^{2}$measurements to guarantee exact recovery of the PSD matrix, where$N$and$K$respectively denote the dimension and the rank of the PSD matrix. Numerical results show that the proposed method achieves a considerable improvement over existing state-of-the-art methods in terms of both sample complexity and computational efficiency. Specifically, the proposed method helps improve the computational efficiency by an order of magnitude as compared with existing methods.

In this paper, we consider the problem of downlink beam training for extremely large-scale millimeter wave (mmWave)/Terahertz (THz) systems, where the far-field assumption which treats wavefronts as planar waves may not hold valid. For such hybrid far/near-field channels, beam training needs to identify the best beam alignment on a two-dimensional angle-range domain. An exhaustive search scheme sequentially scanning the entire angle-range space incurs a high training overhead. To address this issue, in this paper, we propose an efficient hybrid far/near-field beam training method. By utilizing the approximate orthogonality of near-field steering vectors of the same effective distance, we devise a multi-directional beam training sequence which can more efficiently scan the entire angle-range space. Based on the devised beam training sequence, we develop a simple estimation method at the receiver that can simultaneously identify the angle and the range associated with the dominant path. Simulation results show that the proposed method achieves better performance than the exhaustive search scheme, while with a much lower overhead cost. The proposed method also presents a clear advantage over other existing state-of-the-art hybrid far/near-field beam training methods in terms of performance and generality.

Movable antenna (MA) and intelligent reflecting surface (IRS) are considered promising technologies for the next-generation wireless communication systems due to their shared capabilities of reconfiguring and improving wireless channel conditions. This, however, raises a fundamental question: Does the performance gain of MAs over conventional fixed-position antennas (FPAs) still exist in the presence of the IRS passive beamforming? To answer this question, we investigate in this paper an IRS-assisted multi-user multiple-input single-output (MISO) MA system, where a multi-MA base station (BS) transmits to multiple single-FPA users. We formulate a sum-rate maximization problem by jointly optimizing the active/passive beamforming of the BS/IRS and the MA positions within a one-dimensional transmit region, which is challenging to be optimally solved. To drive essential insights, we first study a simplified case with a single user. Then, we analyze the performance gain of MAs over FPAs in the light-of-sight (LoS) BS-IRS channel and derive the conditions under which this gain becomes more or less significant. In addition, we propose an alternating optimization (AO) algorithm to solve the signal-to-noise ratio (SNR) maximization problem in the single-user case by combining the block coordinate descent (BCD) method and the graph-based method. For the general multi-user case, our performance analysis unveils that the performance gain of MAs over FPAs diminishes with typical transmit precoding strategies at the BS under certain conditions. We also propose a high-quality suboptimal solution to the sum-rate maximization problem by applying the AO algorithm that combines the weighted minimum mean square error (WMMSE) algorithm, manifold optimization method and discrete sampling method. Numerical results validate our theoretical analyses and demonstrate that the performance gain of MAs over FPAs may be reduced if the IRS passive beamforming is optimized.

We consider the problem of max-min beamforming (MMB) for cell-free massive multi-input multi-output (MIMO) systems, where the objective is to maximize the minimum achievable rate among all users. Existing MMB methods are mainly based on deterministic optimization methods, which are computationally inefficient when the problem size grows large. To address this issue, we, in this paper, propose a randomized alternating direction method of multiplier (ADMM) algorithm for large-scale MMB problems. We first propose a novel formulation that transforms the highly challenging feasibility-checking problem into a linearly constrained optimization problem. An efficient randomized ADMM is then developed for solving the linearly constrained problem. Unlike standard ADMM, randomized ADMM only needs to solve a small number of subproblems at each iteration to ensure convergence, thus achieving a substantial complexity reduction. Our theoretical analysis reveals that the proposed algorithm exhibits an$O(1/\bar {t})$convergence rate ($\bar {t}$represents the number of iterations), which is on the same order as its deterministic counterpart. Numerical results show that the proposed algorithm offers a significant complexity advantage over existing methods in solving the MMB problem.

We consider the problem of maximizing the minimum user achievable rate for downlink massive MIMO multi-user systems. The max-min formulation promotes fairness such that all users enjoy a similar quality of wireless service. Nevertheless, solving the max-min beamforming problem is challenging due to the max-min form of the objective function. A commonly used method to handle problems of this kind is the bisection method, which transforms the max-min problem into a sequence of feasibility-checking problems. Existing methods for solving the feasibility-checking problem do not scale well since they are usually involved with computing the inversion of a large matrix whose size scales quadratically with the number of users. To efficiently solve the feasibility-checking problem, we propose to reformulate the problem into a two-set feasibility checking problem, which can be efficiently solved via an averaged alternating reflection algorithm (AARA). The proposed AARA method has a fast convergence speed. Moreover, its computational complexity scales linearly with the number of users. Simulation results show that our proposed method significantly outperforms existing methods in terms of computational complexity.

In this work, we consider a multi-server federated learning (FL) framework, referred to as Confederated Learning (CFL), in order to accommodate a larger number of users. To reduce the communication overhead of the CFL system, we propose a linearly convergent stochastic gradient method. The proposed algorithm incorporates a conditionally-triggered user selection (CTUS) mechanism as the central component. Simulation results show that it achieves advantageous communication efficiency over GT-SAGA.

Dual-function radar-communication (DFRC) can alleviate spectrum congestion and competition with the spectrum-sharing architecture for next-generation wireless networks. In this article, we consider the problem of robust beamforming in millimeter wave (mmWave) DFRC systems. Unlike most existing works which assume that the angle-of-arrival (AoA)/angle-of-departure (AoD) or channel state information (CSI) is perfectly known, we consider the case of imperfect CSI resulting from the movement of users/targets or the beam misalignment errors. Our object is to maximize the achievable ergodic rate for communication under the constrained worst-case sensing signal-clutter-noise ratio (SCNR) for radar. By integrating the two-phase-shifter structure into our proposed robust hybrid beamforming architecture, it substantially improves the system performance and increases the design flexibility at the cost of doubling the number of phase shifters. With the two-phase-shifter structure, our proposed robust multibeam technology coherently combines sensing subbeams and communication subbeams with a widebeam radiation pattern for alleviating the effect of AoA/AoD uncertainty. Our proposed robust multibeam beamformer can shape the transmit waveform flexibly with very low complexity, which is amiable for practical implementation. Theoretical and numerical results validate the effectiveness and robustness of our proposed method in mmWave DFRC systems.

In this paper, we consider channel estimation for millimeter wave/Terahertz (mmWave/THz) communication systems equipped with extremely large antenna arrays. As the number of antennas increases, users may locate either in the near-field region or in the far-field region, resulting in a hybrid near/far-field channel model. By analyzing the properties of coherence of two near/far-field steering vectors, we construct an orthogonal dictionary and prove that the hybrid near/far-field channel vector has a block-sparse representation on this dictionary. Based on this observation, hybrid near/far field channel estimation for mmWave/THz systems with extremely large-scale antennas can be formulated as a block-sparsity compressed sensing problem, which can be solved by many block-sparse signal recovery algorithms such as the B-SBL and PC-SBL. Simulation results reveal that our proposed method can achieve a performance improvement over the existing polar-domain based solution with a substantial reduction of training overhead.

We investigate a twin-timescale joint beamforming problem for multiple intelligent reflecting surfaces (IRSs)-assisted multi-user mm Wave orthogonal frequency division multiplexing (OFDM) systems, where the base station (BS) employs a hybrid analog and digital precoder. To alleviate the burden of frequent channel state information (CSI) acquisition and reduce design complexity, we devise the passive beamforming vector and the analog precoder based on statistical CSI, while the digital precoder is designed based on low-dimensional instantaneous CSI. Specifically, the former long-term optimization can be formulated as a stochastic optimization problem. To address this problem, we propose two different solutions. The first method devises the passive beamforming vector and the analog precoder by maximizing the ergodic channel gain. We also propose a deep unrolling-based method to provide a unified framework for the stochastic optimization problem. Our simulation results demonstrate the effectiveness and computational efficiency of the proposed methods.

Intelligent reflecting surface (IRS) has been widely recognized as an efficient technique to reconfigure the electro-magnetic environment in favor of wireless communication performance. In this paper, we propose a new application of IRS for device-free target sensing via joint location and orientation estimation. In particular, different from the existing works that use IRS as an additional anchor node for localization/sensing, we consider mounting IRS on the sensing target, thus estimating the IRS's location and orientation as that of the target by leveraging IRS's controllable signal reflection. To this end, we first propose a three-dimensional beam training method to acquire essential angle information between the IRS and the sensing transmitter as well as a set of distributed sensing receivers. Next, based on the estimated angle information, we formulate two optimization problems to estimate the location and orientation of the IRS/target, respectively, which are solved by invoking the Taylor-series expansion and manifold optimization. Simulation results show that the proposed method can achieve high estimation accuracy and draw useful insights into the performance of target-mounted IRS sensing systems.

Intelligent reflecting surface (IRS) has been widely recognized as an efficient technique to reconfigure the electromagnetic environment in favor of wireless communication performance. In this paper, we propose a new application of IRS for device-free target sensing via joint location and orientation estimation. In particular, different from the existing works that use IRS as an additional anchor node for localization/sensing, we consider mounting IRS on the sensing target, whereby estimating the IRS’s location and orientation as that of the target by leveraging IRS’s controllable signal reflection. To this end, we first propose a tensor-based method to acquire essential angle information between the IRS and the sensing transmitter as well as a set of distributed sensing receivers. Next, based on the estimated angle information, we formulate two optimization problems to estimate the location and orientation of the IRS/target, respectively, and obtain the locally optimal solutions to them by invoking two iterative algorithms, namely, gradient descent method and manifold optimization. In particular, we show that the orientation estimation problem admits a closed-form solution in a special case that usually holds in practice. Furthermore, theoretical analysis is conducted to draw essential insights into the proposed sensing system design and performance. Simulation results verify our theoretical analysis and demonstrate that the proposed methods can achieve high estimation accuracy which is close to the theoretical bound.

To achieve communication-efficient federated multi-task learning (FMTL), we propose an over-the-air FMTL (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). To overcome the inter-task interference inherent in the non-orthogonal transmission among tasks, we design a novel transmission method called model sparsification and random compression (MSRC) as well as a reception method called modified turbo compressed sensing (M-Turbo-CS). More specifically, at each edge device, the local model updates of all tasks are first sparsified andrandomlycompressed with different random compression matrices for different tasks, before being superimposed and sent over the uplink channel. Then the ES constructes the model aggregations of all the tasks from the channel observation data through a modified version of the turbo compressed sensing (Turbo-CS) algorithm called M-Turbo-CS. We analyze the performance of the proposed OA-FMTL framework with MSRC and M-Turbo-CS. Based on the analysis, we formulate a communication-learning optimization problem to improve the system performance by adjusting the power allocation among the tasks at the edge devices. Numerical simulations show that our proposed OA-FMTL efficiently suppresses the inter-task interference to achieve a learning performance comparable to the inter-task interference free bound at a significantly reduced communication overhead. It is also shown that the proposed inter-task power allocation optimization algorithm further reduces the overall communication overhead by appropriately adjusting the power allocation among the tasks.

We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.

In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). Specifically, the model updates for all the tasks are transmitted and superimposed concurrently over a non-orthogonal uplink fading channel, and the model aggregations of all the tasks are reconstructed at the ES through a modified version of the turbo compressed sensing algorithm (Turbo-CS) that overcomes inter-task interference. Both convergence analysis and numerical results show that the OA-FMTL framework can significantly improve the system efficiency in terms of reducing the number of channel uses without causing substantial learning performance degradation.

The sum-rate maximization for intelligent reflecting surfaces (IRS)-aided multi-user MIMO is a recent open problem. The challenge lies in the coefficient designs for reflecting phase shifts (at the IRS) and precoder/decoders (at the BS/users). By imposing two additional constraints, i.e., 1) each IRS only serves one user, 2) no interference exists between users, this paper proposes a novel space-orthogonal scheme for multiple IRSs- aided multi-user MIMO in millimeter wave (mmWave) and terahertz (THz) communications. Based on a new zero-interference criterion, we can successively find high-quality solutions for the IRSs' phase shifts and precoder/decoders one by one. Specifically, we first propose a null-space singular value decomposition (SVD) approach to determine a part of the precoder/decoders. Then, two solutions are developed for IRSs’ phase shifts, namely, the segment matching (SM) and the phase iterative evolution (PIE) solutions. Finally, the remanent part of the precoder/decoders are calculated by SVD with water-filling under the zero-interference constraint. Numerical results demonstrate the effectiveness and superiority of our proposed scheme.

Intelligent reflecting surface (IRS) is a promising technology being considered for future wireless communications due to its ability to control signal propagation. This paper considers the joint active and passive beamforming problem for an IRS-assisted radar, where multiple IRSs are employed to assist the surveillance of multiple targets in cluttered environments. Specifically, we aim to maximize the minimum target illumination power at multiple target locations by jointly optimizing the active beamformer at the radar transmitter and the passive phase-shift matrices at the IRSs, subject to an upperbound on the clutter power at each clutter scatterer. The resulting optimization problem is nonconvex and solved with a sequential optimization procedure along with semidefinite relaxation (SDR). Simulation results show that additional line-of-sight (LOS) paths created by IRSs can substantially improve the radar robustness against target blockage.

Multiple-input multiple-output (MIMO) and intelligent reflecting surface (IRS) are two appealing technologies in millimeter-wave (mmWave) and terahertz (THz) communications. The challenge of combining these two technologies lies in joint design for active beamforming (at the base-station (BS)/users) and passive beamforming (at the IRSs). In this paper, we consider a multi-IRS-aided multi-user MIMO scenario and propose a novel space-orthogonal scheme by applying zero-forcing techniques. Specifically, we first propose a multi-IRS-based zero-interference criterion, under which multi-user interference can be eliminated regardless of the IRS’s phase shifts. Based on this criterion, we decompose the precoder/decoder matrix into a product of two matrices, with one of them devised for interference cancellation and the other one of them devised for achievable rate maximization. Next, an approximate space-orthogonal technique referred to as partial zero-forcing (IRS-PZF) is proposed for proposed for devising the former matrix whose objective is to cancel the multi-user interference; while two efficient phase-shift schemes are proposed for the IRS passive beamforming, namely, water-filling segment matching (WSM) and phase iterative evolution (PIE), which balance between performance and complexity. Finally, we calculate the latter matrix of the precoder/decoder by applying the singular value decomposition (SVD) for the effective BS-user channels, so as to maximize the users’ achievable rates. Numerical results demonstrate the effectiveness and superiority of our proposed scheme compared with the benchmarks.

We study the problem of joint wideband spectrum sensing and recovery of multi-band signals in a multi-antenna-based sub-Nyquist sampling framework. Specifically, the multi-band signal is composed of a number of uncorrelated narrowband signals spreading over a wide frequency band. Unlike existing works which assume the source signals impinge on the receiver via a line-of-sight (LOS) path, we consider a more practical unknown MIMO channel which results from multipath propagation. A new sub-Nyquist sampling architecture is proposed, where each antenna output passes through two channels, namely, a direct path and a delayed path with a controlled amount of time delay. The signal at each channel is then sampled by a synchronized low-rate analog-to-digital converter (ADC). We utilize the collected data samples to build a set of cross-correlation matrices with different time lags and develop a CANDECOMP/PARAFAC (CP) decomposition-based method to recover the carrier frequencies, power spectra as well as the source signals themselves. Recovery conditions of the proposed method are analyzed, and Cramér-Rao bound (CRB) results for our estimation problem are derived. Simulation results are presented to illustrate the effectiveness of the proposed method.

Intelligent reflecting surface (IRS) has emerged as a competitive solution to address blockage issues in millimeter wave (mmWave) and Terahertz (THz) communications due to its capability of reshaping wireless transmission environments. Nevertheless, obtaining the channel state information of IRS-assisted systems is quite challenging because of the passive characteristics of the IRS. In this paper, we consider the problem of beam training/alignment for IRS-assisted downlink mmWave/THz systems, where a multi-antenna base station (BS) with a hybrid structure serves a single-antenna user aided by IRS. By exploiting the inherent sparse structure of the BS-IRS-user cascade channel, the beam training problem is formulated as a joint sparse sensing and phaseless estimation problem, which involves devising a sparse sensing matrix and developing an efficient estimation algorithm to identify the best beam alignment from compressive phaseless measurements. Theoretical analysis reveals that the proposed method can identify the best alignment with only a modest amount of training overhead. Simulation results show that, for both line-of-sight (LOS) and NLOS scenarios, the proposed method obtains a significant performance improvement over existing state-of-art methods. Notably, it can achieve performance close to that of the exhaustive beam search scheme, while reducing the training overhead by 95%.

We consider the problem of joint wideband spectrum sensing and carrier frequency estimation in a sub-Nyquist sampling framework. Specifically, a multi-antenna receiver is used to estimate the carrier frequencies and power spectra of multiple narrowband transmissions that spread over a wide frequency band. Unlike existing works that assume the source signals impinge on the receiver via a line-of-sight (LOS) path, we consider a more practical multiple-input multiple-output (MIMO) channel characterized by multipath propagation. A new sub-Nyquist sampling architecture is proposed, where each antenna output passes through two channels, namely, a direct path and a delayed path with a predetermined time delay. The signal at each channel is then sampled by a synchronized low-rate analog-to-digital converter (ADC). We utilize the collected data samples to build a set of cross-correlation matrices with different time lags and develop a CANDECOMP/PARAFAC (CP) decomposition-based method to recover the carrier frequencies and power spectra of the source signals. Simulation results are presented to illustrate the effectiveness of the proposed method.

We consider the problem of max-min power control for downlink cell-free (CF) massive MIMO systems. Under the bisection framework, solving this problem amounts to solving a sequence of convex conic feasibility checking problems (CCFCP). Unfortunately, the problem size of the CCFCP of the CF massive MIMO system grows rapidly as the number of users and access points (AP) increases. Existing feasibility checking methods become computationally intractable even when the system consists of only a moderate number of users and APs. To address this limitation, we propose to reformulate the CCFCP as a two-set feasibility problem, which is then solved by the averaged alternating reflection (AAR) algorithm. The proposed method outperforms existing methods in terms of computational efficiency.

In this article, we consider the problem of decentralized composite optimization over a connected and symmetric graph, in which each node holds its own agent-specific private convex functions, and communications are only allowed between nodes with direct links. A variety of algorithms has been proposed to solve such a problem in an alternating direction method of multiplier (ADMM) framework. Many of these algorithms, however, need to include some extra proximal term in the augmented Lagrangian function such that the resulting algorithm can be implemented in a decentralized manner. The use of the extra proximal term slows down the convergence speed because it forces the current solution to stay close to the solution obtained in the previous iteration. To address this issue, in this article, we first introduce the notion of simplest bipartite graph, which is defined as a bipartite graph that has a minimum number of edges to keep the graph connected. A simple two-step message passing-based procedure is proposed to find a simplest bipartite graph associated with the original graph. We show that the simplest bipartite graph has some interesting properties. By utilizing these properties, an ADMM without involving extra proximal terms can be developed to perform decentralized composite optimization over the simplest bipartite graph. The simulation results show that our proposed method achieves a much faster convergence speed than the existing state-of-the-art decentralized algorithms.

Intelligent reflecting surface (IRS) has emerged as an appealing solution to enhance wireless communication performance by reconfiguring the wireless propagation environment. In this paper, we propose to apply IRS to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem, which is shown equivalent to a secrecy rate maximization problem subject to the constraints on the quality of multicast service. Due to the non-convexity of this problem, we propose two customized algorithms to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. The resulting performance gap with the globally optimal solution is analyzed. Furthermore, we provide theoretical analysis to unveil the impact of IRS beamforming on the performance of PHY-SI. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI and also validate our theoretical analysis.

Large intelligent surface (LIS) has recently emerged as a potential low-cost solution to reshape the wireless propagation environment for improving the spectral efficiency. In this article, we consider a downlink millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) system, where an LIS is deployed to assist the downlink data transmission from a base station (BS) to a user equipment (UE). Both the BS and the UE are equipped with a large number of antennas, and a hybrid analog/digital precoding/combining structure is used to reduce the hardware cost and energy consumption. We aim to maximize the spectral efficiency by jointly optimizing the LIS's reflection coefficients and the hybrid precoder (combiner) at the BS (UE). To tackle this non-convex problem, we reformulate the complex optimization problem into a much more friendly optimization problem by exploiting the inherent structure of the effective (cascade) mmWave channel. A manifold optimization (MO)-based algorithm is then developed. Simulation results show that by carefully devising LIS's reflection coefficients, our proposed method can help realize a favorable propagation environment with a small channel matrix condition number. Besides, it can achieve a performance comparable to those of state-of-the-art algorithms, while at a much lower computational complexity.

We study the problem of downlink beamforming training and channel estimation for millimeter wave (mmWave) OFDM systems, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station) and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). To efficiently probe the channel, we form multiple directional beams simultaneously at the transmitter and steer them towards different directions. The objective is to devise the beam training sequence and develop an efficient algorithm to estimate the channel. By exploiting the sparse scattering nature of mmWave channels, the above problem is formulated as one of sparse encoding and signal recovery, which involves finding a sparse sensing matrix to compress the sparse channel and an efficient channel estimation algorithm to recover the sparse channel from compressive measurements. In this article, we propose a sparse bipartite graph code-based algorithm, where a set of bipartite graphs are employed to encode the sparse channel and a simple decoding procedure that relies on the presence of a No-Multiton-graph (NM-graph) is used to reconstruct the sparse channel. Theoretical analysis shows that our proposed method can help achieve a substantial training overhead reduction. Simulations are provided to show the effectiveness of the proposed algorithm and its performance advantage over compressed sensing-based methods.

By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks. In this paper, we study a RIS aided multiuser multiple-input single-output (MISO) wireless power transfer (WPT) system, where the transmitter is equipped with a constant-envelope analog beamformer. We formulate a novel problem to maximize the total received power of all the users by jointly optimizing the beamformer at transmitter and the phase shifts at the RISs, subject to the individual minimum received power constraints of users. We further solve the problem iteratively with a closed-form expression for each step. Numerical results show the performance gain of deploying RIS and the effectiveness of the proposed algorithm.

This paper concerns wideband direction of arrival (DoA) estimation with sparse linear arrays (SLAs). We rely on the assumption that the power spectrum of the wideband sources is the same up to a scaling factor, which could in theory allow us to resolve not only more sources than the number of antennas but also more sources than the number of degrees of freedom (DoF) of the difference co-array of the SLA. We resort to the Jacobi-Anger approximation to transform the coarray response matrices of all frequency bins into a single virtual uniform linear array (ULA) response matrix. Based on the obtained model, two super-resolution DoA estimation approaches based on atomic norm minimization (ANM) are proposed, one with and one without prior knowledge of the power spectrum. Simulation results show that our proposed methods outperform the state of the art and are indeed capable of resolving more sources than the number of DoF of the difference co-array.

In this letter, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.

Instrument recognition plays very important roles in music information retrieval, sound source separation and automatic music transcription. However, due to different playing styles and audio qualities, this task cannot be accomplished easily. Simultaneous existence of multiple instruments in polyphonic music increases the challenge to a greater extent. This article mainly focus on the identification of the predominant instruments in polyphonic music. We propose to construct a network with an auxiliary classification designed based on the onset groups and instrument families. The principal classification and the auxiliary classification enable the network to learn the instrument categories and groups jointly in a pattern of multitask learning. The IRMAS datasetis adopted in the experiment to extract the mel-spectrogram and six other types of features. The micro and macro average of precisions, recalls and F1 measures are used to evaluate the classification results. The effect of multitask learning, batch normalization and center loss in the predominant instrument recognition are demonstrated by various experiments. By selecting the loss ratios through a development set, the micro and macro F1 measures of our proposed network can reach 0.685 and 0.597, which are 10.7% and 16.4% higher than those obtained by the baseline, the ConvNet presented in [1].

In this paper, we consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. We first extend the conventional Bussgang linear minimum mean square error (BLMMSE) estimator to the general nonzero threshold case. We then study the problem of one-bit quantization design, aiming at minimizing the mean squared error of the generalized BLMMSE estimator. A set partition scheme is proposed to devise the quantization thresholds. The rationale behind the proposed scheme is to divide each antenna's received samples into a number of disjoint subsets according to their pairwise correlation and assign diverse thresholds to those highly correlated data samples. In addition to the set partition scheme, a gradient descent scheme is developed to search for optimal quantization thresholds. The proposed schemes only require the statistical information of the received signals to devise the quantization thresholds, which can be calculated in advance before the training process begins. Simulation results show that the generalized BLMMSE estimator can achieve a significant performance improvement over the conventional Bussgang LMMSE estimator.

There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

We consider the problem of channel estimation for uplink massive multiple-input multiple-output systems, where one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. In this paper, we study the problem of one-bit quantizer design when a Bussgang linear minimum mean square error (BLMMSE) estimator is used for channel estimation. We first extend the conventional Bussgang LMMSE estimator \cite{LiTao17} to the general nonzero threshold case. We then analyze the estimation performance of the generalized Bussgang LMMSE estimator and investigate the design of quantization thresholds. A gradient descent scheme is developed to search for optimal quantization thresholds. Simulation results show that, with carefully devised quantization thresholds, the generalized Bussgang LMMSE estimator can achieve a substantial performance improvement over the conventional Bussgang LMMSE estimator.

This paper exploits the potential of millimeter wave (mmWave) system, where large-scale antenna arrays are allowed to implement in small physical dimension. We investigate a novel hybrid beamforming design for joint data and artificial noise (AN) precoding and power fraction selection in massive multi-input multi-output (MIMO) system. We aim at the secrecy rate maximization problem with respect to hybrid precoders design. The challenge of this problem lies in its non-convexity. To address this issue, we decouple the design for analog and digital precoders. We conduct analog precoder to maximize corresponding channel gain. For digital data precoder design, we first remove the non-convex codebook constraint and propose an iterative algorithm for optimal equivalent digital precoder design. Then, reconsidering the constraint, we conduct the digital data precoder to approach to the optimal design. Next, aiming to maximize AN power aligned at the eavesdropper, AN precoder design is optimally derived in closed form. Finally, we get power fraction by one-dimensional (1-D) search. Simulation results indicate that our proposed AN- aided hybrid precoding scheme achieves better secrecy performance compared with existing hybrid precoding schemes.

The problem of beam alignment for millimeter wave (mm-Wave) communications is studied in this paper. We show that, by exploiting the sparse scattering nature of mmWave channels, the beam alignment problem can be formulated as a sparse encoding and phaseless decoding problem, which involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of the s-parse signal from compressive phaseless measurements. We develop a general function-Code (GF-Code) algorithm for s-parse encoding and phaseless decoding. Simulation results are provided to corroborate the effectiveness of the proposed GF-Code method.

We propose DGG: Deep clustering via a Gaussian-mixture variational autoencoder (VAE) with Graph embedding. To facilitate clustering, we apply Gaussian mixture model (GMM) as the prior in VAE. To handle data with complex spread, we apply graph embedding. Our idea is that graph information which captures local data structures is an excellent complement to deep GMM. Combining them facilitates the network to learn powerful representations that follow global model and local structural constraints. Therefore, our method unifies model-based and similarity-based approaches for clustering. To combine graph embedding with probabilistic deep GMM, we propose a novel stochastic extension of graph embedding: we treat samples as nodes on a graph and minimize the weighted distance between their posterior distributions. We apply Jenson-Shannon divergence as the distance. We combine the divergence minimization with the log-likelihood maximization of the deep GMM. We derive formulations to obtain an unified objective that enables simultaneous deep representation learning and clustering. Our experimental results show that our proposed DGG outperforms recent deep Gaussian mixture methods (model-based) and deep spectral clustering (similarity-based). Our results highlight advantages of combining model-based and similarity-based clustering as proposed in this work. Our code is published here: https:// github.com/dodoyang0929/DGG.git.

Grant-free non-orthogonal multiple access (NOMA) has recently gained significant attention for reducing signaling overhead in machine-type communications (MTC). In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame based multi-user uplink scenarios where users are (in)active for the duration of a frame, namely frame-wise joint sparsity model. Firstly, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and consider that the user sparsity level should be unknown for multiuser detection, two enhanced BCS- based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross validation aided block sparsity adaptive subspace pursuit (CVA- BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance, by reasonably setting the threshold based on the AWGN noise floor. And the proposed CVA-BSASP algorithm is a highly practical algorithm design that does not require any prior knowledge, by adopting the statistical and machine learning mechanism cross validation (CV) to determine the stopping condition of the algorithm. Superior performance of the proposed algorithms is demonstrated by numerical experiments.

In this letter, we propose a proximal alternating direction method of multiplier (ADMM) to solve the composite optimization problem over a decentralized network. Compared with existing methods, such as PG-EXTRA and IC-ADMM, the proposed decentralized proximal ADMM method does not rely on assuming a smooth + nonsmooth structure on the objective functions, thus covering a wider range of composite optimization problems. Simulation results show that the proposed proximal ADMM presents a considerable performance advantage over existing state-of-the-art algorithms for both nonsmooth + nonsmooth and smooth + nonsmooth composite optimization problems.

We consider the nonlinear robust filtering problem where the measurements are partially disturbed by outliers. A new robust Kalman filter based on a detect-and-reject idea is developed. To identify and exclude outliers automatically, each measurement is assigned an indicator variable, which is modeled by a beta-Bernoulli prior. The mean-field variational Bayesian method is then utilized to estimate the state of interest as well as the indicator in an iterative manner at each time instant. Simulation results reveal that the proposed algorithm outperforms several recent robust solutions with higher computational efficiency and better accuracy.

This paper investigates and quantifies the advantages of a Full-Duplex (FD) transmitter/receiver pair in improving the secrecy rate of the system. We consider a linear precoder design for a multiple-input multiple-output Gaussian wiretap channel, which comprises two legitimate nodes, i.e., Alice and Bob, operating in FD mode and exchanging confidential messages in the presence of a passive eavesdropper. Using the sum secrecy degrees of freedoms (sum SDoFs) as metric, we formulate an optimization problem with respect to Alice's and Bob's precoding matrices. In order to solve this problem, we first propose a cooperative secrecy transmission scheme, whose feasible set is sufficient to achieve the maximum sum SDoF. Based on that feasible set, we then determine in closed form the maximum achievable sum SDoF and also provide a method for constructing the precoding matrix pair, which achieves the maximum sum SDoF. The latter pair would be near-optimal in terms of the achievable secrecy sum rate in the high signal-to-noise ratio (SNR) regime. By providing the maximum achievable sum SDoF as a function of the number of antennas, one could select the optimal system parameters to further maximize the achievable sum SDoF. We use simulations to evaluate the performance of the proposed precoding matrices in realistic channel scenarios and at various levels of the SNR.

Grant-free non-orthogonal multiple access has recently gained significant attention for reducing signaling overhead in machine-type communications. In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame-based multi-user uplink scenarios where users are (in)-active for the duration of a frame, namely, the frame-wise joint sparsity model. First, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and considering the user sparsity level to be unknown for multiuser detection, two enhanced BCS-based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross-validation aided block sparsity adaptive subspace pursuit (CVA-BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance by reasonably setting the threshold based on the additive white Gaussian noise floor. Moreover, the proposed CVA-BSASP algorithm is a highly practical algorithm design that adopts the statistical and machine learning mechanism cross-validation to determine the stopping condition of the algorithm and this does not require prior knowledge. Furthermore, the convergence and the computational complexity of the proposed algorithms are derived and the superior performance of the proposed algorithms is demonstrated by numerical experiments.

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio frequency chain at the base station and mobile station is employed. Most existing works for mmWave channel estimation exploit sparse scattering characteristics of the channel. In addition to sparsity, mmWave channels may exhibit angular spreads over the angle of arrival, angle of departure, and elevation domains. In this paper, we show that angular spreads give rise to a useful low-rank structure that, along with the sparsity, can be simultaneously utilized to reduce the sample complexity, i.e., the number of samples needed to successfully recover the mmWave channel. Specifically, to effectively leverage the joint sparse and low-rank structure, we develop a two-stage compressed sensing method for mmWave channel estimation, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage. Our theoretical analysis reveals that the proposed two-stage scheme can achieve a lower sample complexity than a conventional compressed sensing method that exploits only the sparse structure of the mmWave channel. Simulation results are provided to corroborate our theoretical results and to show the superiority of the proposed two-stage method.

In this paper, we propose a new channel estimation scheme for TDD/FDD massive MIMO systems by reconstructing (sometimes also referred to as covariance computing or covariance fitting) uplink/downlink channel covariance matrices (CCMs) with the aid of array signal processing techniques. Specifically, the angle parameters and power angular spectrum (PAS) of channel are extracted from the instantaneous uplink channel state information (CSI). Then, the uplink CCM is reconstructed and can be used to improve the uplink channel estimation without any additional training cost. By virtue of angle reciprocity as well as PAS reciprocity between uplink and downlink channels, the downlink CCM could also be inferred with a similar approach even for the FDD massive MIMO systems. Then, the downlink instantaneous CSI can be obtained by training toward the dominant eigen-directions of each user. The proposed strategy is applicable to various PAS distributions. Numerical results are provided to demonstrate the superiority of the proposed methods over the existing ones.

This paper considers a cellular multiple-input single- output (MISO) system overheard by multiple eavesdroppers, in the presence of one pair of single- antenna device-to-device (D2D) nodes working as an underlay. The D2D nodes are permitted to access the cellular channel for their own communications. We assume that the channel state information (CSI) on all links is imperfect, and more specifically, the CSI error follows an arbitrary distribution with only the first and second moments available at the transmitter. Our goal is to design the covariances of confidential message and artificial noise, as well as the transmit power at the D2D transmitter, such that the total consumed power is minimized subject to a sequence of worst-case outage constraints on the received signal- to-interference-plus-noise ratio (SINR) at each receiver. The worst-case outage constraints are imposed to satisfy SINR outage requirement under arbitrarily distributed CSI uncertainty. The resulting problem is challenging to solve due to its inherently complex structure. However, we reveal its hidden convexity by carrying out a duality-based reformulation. Then it is proved that the proposed method always yield a single- stream beamforming solution. The complexity analysis of our proposed method is also presented. Finally, the efficacy of the proposed design is demonstrated by simulations.

Today multiple radio access technologies (RATs) coexist in wireless networks. Multi-mode mobile terminals (MMTs) which can switch between networks with different RATs can enjoy enhanced quality of service (QoS) by exploring the diversity among different radio access networks (RANs). Usually, it has been assumed in literature that an MMT can access only one network at a time. Multi-Radio Access (MRA) is a technology that allows an MMT to transmit and receive data via multiple RANs simultaneously. With MRA, users can combine the data streams from multiple networks to meet their throughput requirements and enjoy customized QoS. In this paper, we consider an MMT with MRA technology in a heterogeneous network environment. It is assumed that the connection session of the MMT will spread over multiple handover windows and that the MMT is allowed to switch from the present set of connected RANs to another set at each handover window. We are interested in designing an intelligent network switching strategy which maximizes the average cumulative utility function of the MMT. Taking into account the dynamics of the heterogeneous networks, we model the network selection problem as a Markov decision process (MDP). By using the value iteration algorithm, we obtain the optimal switching strategy. Simulation results show that the MDP method provides higher average cumulative utility function and higher average rate of minimum throughput satisfaction than greedy method.

This paper considers a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel model with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver and kept perfectly secure from the other receiver. Our goal is to jointly design the transmit covariances of the multicast message and confidential message, such that the secrecy capacity region is maximized. This maximization problem is a biobjective optimization problem, but can be converted into a general scalar optimization problem via our proposed method of scalarization. Nonetheless, the equivalent scalar problem is nonconvex by nature. To circumvent the nonconvex issue, a provably convergent difference-of-concave (DC) approach is introduced to solve it in an iterative fashion. In view of the high computational complexity of the DC approach, a power splitting method is also devised for fast implementation of service integration. The security performance and computational efficiency of our proposed algorithms are finally demonstrated by numerical results.

This paper concerns detecting the frequency components from a spectral sparse, undersampled signal. This problem is also called super-resolution line spectral estimation because the frequencies can take arbitrary continuous values. The prior knowledge of the frequency distribution is often available in many applications. To exploit the prior knowledge, a weighting function w(f) designed according to the frequency distribution p(f) is introduced. The prior information can be harnessed through minimizing the corresponding weighted log-sum penalty function. We solve the optimization problem through iteratively decreasing a surrogate function majorizing the original penalty function. Simulation results show that the proposed algorithm outperforms other methods both in noiseless and noisy case, and it also presents superior performance in resolving closely-spaced frequency components.

This paper considers a cellular multiple-input multiple-output multiple-eavesdropper (MIMOME) channel, with a pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication and the D2D communication. Our goal is to jointly optimize the covariance of confidential message and artificial noise, as well as the transmit power at the D2D transmitter, such that the sum secrecy rate is maximized, while satisfying the quality of service constraint on the D2D communication. This sum secrecy rate maximization (SSRM) problem is non-convex by nature. To handle it, an equivalent reformulation of this SSRM problem is introduced, wherein the resulting problem becomes primal decomposable and thus can be iteratively solved using an alternating optimization (AO) algorithm. Also, we prove that the AO algorithm is bound to converge to a stationary point of the primal SSRM problem. Furthermore, we extend the SSRM problem to a more general case with multiple pairs of D2D nodes. Again, the resulting problem is shown to be solvable by the AO algorithm, with provable convergence to the stationary point. Finally, numerical results are presented to verify the efficacy of our proposed method.

Consider a cellular multiple-input single-output (MISO) channel, in the presence of multiple eavesdroppers (Eves) and one pair of single-antenna device-to-device (D2D) nodes working as an underlay. A novel eavesdropping scenario, termed as selective eavesdropping, is studied in this paper, where Eves arbitrarily select one target from the cellular receiver and the D2D receiver to overhear, but their selection is unknown to any other nodes. Since Eves' two sorts of selection would lead to two different secrecy rates, we define the achievable secrecy rate as the smaller one of the two rates. With imperfect channel state information on all links, our interest lies in the robust design of transmit covariances at the cellular transmitter, such that the worst-case achievable secrecy rate is maximized. This worst-case secrecy rate maximization problem is nonconvex by nature. To deal with it, we develop a convex approximation to seek a computationally efficient lower bound. In particular, the solution can be efficiently computed by successively solving a sequence of convex optimization problems. Then it is proved that the obtained lower bound is attainable by simply utilizing single-stream beamforming. Numerical results are finally presented to demonstrate the efficacy of our proposed methods.

In this paper, channel estimation in millimeter wave (mmWave) communication systems is considered. In contrast to prevailing mmWave channel estimation methods exploiting the sparsity nature of the channel, we move one step further by exploiting the joint AoD-AoA angular spread. By formulating the channel estimation as a block-sparse signal recovery with an underlying two-dimensional cluster feature, we propose a two-dimensional sparse Bayesian learning method without a priori knowledge of two-dimensional angular spread patterns. It essentially couples the channel path power at one angular direction with its two-dimensional AoD-AoA neighboring directions. Compared with existing sparse mmWave channel estimation methods, the proposed method is numerically verified to reduce the training overhead and channel estimation error.

In a multi-antenna time-division duplex (TDD) communication system, due to channel reciprocity, the downlink channel state information can be obtained by conducting uplink training. In a wire-tap channel, an active eavesdropper can perform active eavesdropping by pilot spoofing attack. In such an attack, the eavesdropper, during the uplink training phase, transmits the identical pilot sequence as that of the legitimate receiver to the transmitter. As a result, the estimated channel by the transmitter is a weighted sum of the legitimate channel and the eavesdropping channel. Motivated by the seriousness of pilot spoofing attack, in this paper, we propose a two-stage uplink training method for pilot spoofing attack detection and secure transmission. Using the new training method, the legitimate channel and the eavesdropping channel can be correctly estimated separately. Then we propose a pilot spoofing attack detector followed by a beamforming scheme for secure data transmission. Simulation results have shown that our proposed method achieves higher detection probability, and larger secrecy rate than previously proposed anti-pilot spoofing methods.

Machine-to-Machine (M2M) communication plays a significant role in supporting Internet of Thing (IoT). This paper is concerned about multiuser detection (MUD) for massive M2M supported by Low-Activity Code Division Multiple Access (LA-CDMA). In previous work, maximum likelihood (ML) and maximum a posterior probability (MAP) detectors have been developed for such system. The ML detector has exponential complexity, while the MAP detector requires perfect knowledge of user activity factor. In practice, the user activity factor may not be known and could change from time to time. To design MUD detectors addressing these problems, in this paper, we formulate multiple measurement vector (MMV) model for uplink LA-CDMA system with time-varying user activities. Since the transmitted signals have block sparse structure, we introduce the pattern coupled spare Bayesian learning (PCSBL) by using the neighbour coherence of each transmitted signal, which effectively solves the user activity factor unknown problem. Furthermore, we embed the generalized approximate message passing (GAMP) to PCSBL and develop a novel algorithm, called generalized approximate message passing pattern coupled sparse Bayesian learning (GAMP-PCSBL). The GAMP-PCSBL does not require activity factor either, and greatly reduces the computational complexity. Simulation results have shown that the proposed algorithms have superior recovery performance than the conventional algorithms.

This paper considers an energy-efficient transmit design in a multiple-input single-output (MISO) wiretap channel. In particular, a transmitter sends one confidential message to a legitimate receiver, which must be kept perfectly secure from multiple external single-antenna eavesdroppers. Assuming statistical eavesdroppers' channel state information (ECSI) at the transmitter, we aim to design the transmit beamformer, such that the outage secrecy energy efficiency (SEE) is maximized, subject to the outage-constrained secrecy rate and transmit power constraints. The resultant problem is intractable to solve even after introducing a semidefinite relaxation (SDR) reformulation. To handle it, an equivalent parametric reformulation, based on the fractional programming and difference-of-concave programming theories, is proposed to recast this problem as a convex problem. By this means, the maximum outage SEE can be found in an iterative fashion. Moreover, we also give an approach to constructing a rank-one covariance matrix from our proposed method, implying the feasibility of transmit beamforming to achieve the obtained SEE performance. Numerical results are presented to show the effectiveness of our proposed method.

This paper considers a cellular multiple-input single- output (MISO) system overheard by multiple eavesdroppers, in the presence of one pair of device- to-device (D2D) nodes working as an underlay to the cellular network. A novel eavesdropping scenario is studied in this paper, where the eavesdroppers intend to simultaneously overhear the cellular communication as well as the D2D communication. Assuming imperfect channel state information (CSI) at the transmitter, our goal is to design the input covariance matrix of confidential message such that the worst-case sum secrecy rate is maximized, while satisfying the quality of service (QoS) requirement in the D2D communication. Although this worst-case sum secrecy rate maximization (SSRM) problem is non-convex, we show that it can be handled by solving a sequence of semidefinite programming (SDP) problems. Moreover, we give complexity analysis of our proposed optimization method and prove that transmit beamforming is an optimal strategy for the confidential message transmission. Numerical results are presented to verify the efficacy of our proposed method.

In this paper, the downlink channel estimation for millimeter wave (mmWave) MIMO systems over frequency selective channels is considered, where both the base station (BS) and the mobile station (MS) are equipped with massive number of antennas. We assume hybrid analog and digital beamforming structures are employed at BS and MS. To overcome the frequency selective fading, we employ orthogonal frequencydivision multiplexing (OFDM) in transmission. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for downlink channel estimation. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. Simulation results show that the proposed method presents a clear advantage over the compressed sensing-based method in terms of both estimation accuracy and computational complexity.

We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the MS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramér-Rao bound (CRB) results for channel parameters and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs and present a clear advantage over the compressed sensing-based method.

Sparse Beyesian learning is a popular approach for sparse signal recovery, and has demonstrated superior performance in a series of experiments. Nevertheless, the sparse Bayesian learning algorithm involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size, which hinders its application to many practical problems even with moderately large datasets. To address this issue, in this letter, we develop a fast inverse-free sparse Bayesian learning method. Specifically, by invoking a fundamental property for smooth functions, we obtain a relaxed evidence lower bound (relaxed-ELBO) that is computationally more amiable than the conventional ELBO used by sparse Bayesian learning. A variational expectation-maximization (EM) scheme is then employed to maximize the relaxed-ELBO, which leads to a computationally efficient inverse-free sparse Bayesian learning algorithm. Simulation results show that the proposed algorithm has a fast convergence rate and achieves lower reconstruction errors than other state-of-the-art fast sparse recovery methods in the presence of noise.

This letter concentrates on the problem of spectral compressed sensing in impulsive noise, which aims to recover a spectrally sparse signal from its contaminated and undersampled measurements. We propose a robust formulation for joint sparse signal and frequency recovery, which includes the generalized ℓpnorm(02approach via majorizing the original objective function by a quadratic surrogate function. Simulation results illustrate that the proposed approach attains a significant performance improvement over the existing methods under impulsive noise.

We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large number of antennas serves a number of single-antenna users simultaneously. To obtain the channel state information (CSI) at the BS in FDD systems, the downlink channel has to be estimated by users via downlink training and then fed back to the BS. For FDD large-scale MIMO systems, the overhead for downlink training and CSI uplink feedback could be prohibitively high, which presents a significant challenge. In this paper, we study the behavior of the minimum mean-squared error (MMSE) estimator when the channel covariance matrix has a low rank or an approximate low-rank structure. Our theoretical analysis reveals that the amount of training overhead can be substantially reduced by exploiting the low-rank property of the channel covariance matrix. In particular, we show that the MMSE estimator is able to achieve exact channel recovery in the asymptotic low-noise regime, provided that the number of pilot symbols in time is no less than the rank of the channel covariance matrix. We also present an optimal pilot design for the single-user case, and an asymptotic optimal pilot design for the multi-user scenario. Last, we develop a simple model-based scheme to estimate the channel covariance matrix, based on which the MMSE estimator can be employed to estimate the channel. The proposed scheme does not need any additional training overhead. Simulation results are provided to verify our theoretical results and illustrate the effectiveness of the proposed estimated covariance-assisted MMSE estimator.

In this paper, a new iteratively reweighted least squares method is proposed for recovery of block-sparse signals with unknown cluster patterns. In many practical applications, sparse signals have block-sparse structures with nonzero coefficients occurring in clusters, while the prior information of the cluster pattern is usually unavailable. To address this issue, we propose an element-overlapping log-sum functional to encourage the sparseness and the cluster pattern simultaneously. The algorithm is developed by iteratively minimizing a convex surrogate function that majorizes the original objective function, which results in an iteratively reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Convergence of the iterations to a local minimum of the penalty function is also guaranteed. Numerical results are provided to illustrate the effectiveness of the proposed method.

This paper considers secrecy communication from a signal processing point of view, and studies the maximal achievable secrecy degrees of freedoms (S.D.o.F.) of a helper-assisted Gaussian wiretap channel, consisting of a source, a legitimate receiver, an eavesdropper and an external helper. Each terminal is equipped with multiple antennas. We first propose a cooperative secrecy transmission scheme, and show that it achieves the maximal secrecy degrees of freedom. We then propose a heuristic method, through which, we solve analytically the optimization problem associated with the proposed cooperative secrecy transmission scheme. By this way, we obtain the maximal achievable S.D.o.F. and also the precoding matrices which achieve the maximal S.D.o.F. in closed-form.

This paper considers adaptive signal detection in stochastic homogeneous environments where the disturbance covariance matrix of both test and training signals, R, is assumed to be a random matrix with a priori knowledge of R. Unlike existing detectors assuming a known hyperparameter associated with R, a knowledge-aided detector with the capability of automatic weighting is considered by accounting for the uncertainty of the prior knowledge. Specifically, the generalized likelihood ratio test (GLRT) is utilized to develop the test statistic, along with the maximum marginal likelihood (MML) estimation of the hyperparameter. The proposed KA-MML-GLRT detector is evaluated by numerical simulations and the results show improved detection performance over conventional and knowledge-aided detectors, especially in the case of limited training signals and inaccurate prior knowledge.

We consider a dictionary learning problem aimed at designing a dictionary such that the signals admits a sparse or an approximate sparse representation over the learned dictionary. The problem finds a variety of applications including image denoising, feature extraction, etc. In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation. Suitable non-informative priors are also placed on the dictionary and the noise variance such that they can be reliably estimated from the data. Based on the hierarchical model, a Gibbs sampling method is developed for Bayesian inference. The proposed method have the advantage that it does not require the knowledge of the noise variance a priori. Numerical results show that the proposed method is able to learn the dictionary with an accuracy better than existing methods.

Machine-to-Machine (M2M) communications will be playing an important role in the development of 5th generation (5G) and future wireless communication systems. Due to the sporadic nature of massive access, Low-Activity Code Division Multiple Access (LA-CDMA) is one of possible multiple access schemes for M2M communications. In the literature, maximum a posterior (MAP) detector has been proposed to detect the active users when the user activity factor is known and small. However, the user activity factor is usually unknown and could be large in practice, which makes the multiuser detection (MUD) a challenging task for LA-CDMA. In this paper, we first introduce sparse Bayesian learning (SBL) method to recover the transmitted signals for LA- CDMA uplink access. The proposed method exploits the sparsity of the transmitted signals and does not require the knowledge of user activity. Furthermore, we add on the known finite-alphabet constraints and introduce Gaussian mixture model (GMM) method to obtain the transmitted signals. Simulation results have shown that the proposed methods outperform the conventional algorithms.

Conventionally, fast frequency hopping (FFH) is regarded as a non‐coherent system, which has inevitable shortcomings in low spectral efficiency. To achieve a high spectral efficiency while maintaining a favourable bit error ratio (BER) performance in FFH systems, in this study, the authors extend their previously proposed subset‐based coherent FFH (S‐CFFH) scheme with the aid of spatial modulation (SM). Furthermore, they derive the closed‐form expressions of the pairwise error probability and asymptotically BER bound for the S‐CFFH/SM maximum‐likelihood receivers in the presence of multitone jamming and highly frequency‐selective Rayleigh fading channels, where the perfect and imperfect channel state information are both considered. The authors’ analysis and simulation results show that, the proposed S‐CFFH/SM scheme outperforms both the conventional non‐coherent FFH and the S‐CFFH schemes, in terms of spectral efficiency and BER, respectively.

This letter considers secrecy communication from an information-theoretic perspective, and studies the secrecy capacity of a multi-input multi-output (MIMO) Gaussian wiretap channel with a source (Alice), an eavesdropper (Eve) and a Full-Duplex (FD) legitimate receiver (Bob). Bob can allocate part of his antennas to transmit jamming signals to impair Eve’s channel. Our goal is to identify the secrecy capacity behavior in the high signal-to-noise ratio (SNR) regimes, i.e., the maximal achievable secure degrees of freedom (S.D.o.F). Such S.D.o.F maximization is generally difficult to solve since it requires to face a nonlinear fractional problem. To deal with this issue, we first propose a cooperative secrecy transmission scheme, and prove its optimality in the sense of achieving the maximal S.D.o.F.. By studying this proposed transmission scheme, we obtain the maximal achievable S.D.o.F. in closed form for any given antenna allocation at Bob. Based on this closed-form result, we further analytically derive the optimal antenna allocation at Bob. To the best of our knowledge, this is the first time that the benefit brought by using the FD jamming Bob has been quantified.

This letter considers a two-receiver multiple-input single-output Gaussian broadcast channel model with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for both receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from the unauthorized receiver. Our goal is to jointly design the input covariances for the multicast message and confidential message, such that the secrecy capacity region is maximized. This secrecy capacity region maximization (SCRM) problem is a nonconvex vector maximization problem. To deal with this issue, we reformulate the SCRM problem into a provably equivalent scalar optimization problem and propose a searching method to find its overall Pareto optimal points. Further, for implementation efficiency, transmit beamforming is proved to be Pareto optimal. However, since the two service messages are coupled in our optimization problem, it is difficult to deduce closed-form expressions of the Pareto optimal beamformers. A suboptimal transmit design is accordingly proposed to analytically obtain beamformers for both service messages. Numerical results illustrate that the performance gap between the Pareto optimal design and our proposal is negligible.

This letter considers a two-receiver multiple-input multiple-output Gaussian broadcast channel model with integrated services. Specifically, we combine two sorts of service messages, and serve them simultaneously: One multicast message intended for both receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from the unauthorized receiver. DueAN54-B6010-A001 to the coupling of service messages, it is intractable to seek capacity-achieving transmit covariance matrices. Accordingly, we propose a suboptimal precoding scheme based on the generalized singular value decomposition (GSVD). The GSVD produces several virtual orthogonal subchannels between the transmitter and the receivers. Subchannel allocation and power allocation between multicast message and confidential message are jointly optimized to maximize the secrecy rate in this letter, subject to the quality of multicast service constraints. Since this problem is inherently complex, a difference-of-concave algorithm, together with an exhaustive search, is exploited to handle the power allocation and subchannel allocation, respectively. Numerical results are presented to illustrate the efficacy of our proposed strategies.

We consider the problem of recovering block-sparse signals with intrablock correlated entries. The block partition of the sparse signal is assumed unknown a priori. To exploit the block-sparse structure as well as the local smoothness of the sparse signal, consecutive coefficients of the sparse signal are organized into a number of 2×2 matrices, and the log-determinant function is used to promote the low rankness of these 2×2 matrices. We show that such a log-determinant function has the ability to promote the block-sparsity and local smoothness simultaneously. An iterative reweighted method is developed by iteratively minimizing a surrogate function of the original objective function. Simulation results show that our proposed method offers competitive performance for recovering block-sparse signals with intrablock correlated entries.

We consider the problem of recovering 2D block-sparse signals with unknown cluster patterns. The 2D block-sparse patterns arise naturally in many practical applications, such as foreground detection and inverse synthetic aperture radar imaging. To exploit the underlying block-sparse structure, we propose a 2D pattern-coupled hierarchical Gaussian prior model. The proposed pattern-coupled hierarchical Gaussian prior model imposes a soft coupling mechanism among neighboring coefficients through their shared hyperparameters. This coupling mechanism enables effective and automatic learning of the underlying irregular cluster patterns, without requiring any a priori knowledge of the block partition of sparse signals. We develop a computationally efficient Bayesian inference method, which integrates the generalized approximate message passing technique with the proposed prior model. Simulation results show that the proposed method offers competitive recovery performance for a range of 2D sparse signal recovery and image processing applications over the existing method, meanwhile achieving a significant reduction in the computational complexity.

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to K users, the problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancellation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees of freedom of the transmit array. The sought-after signal is treated as a random vector with a hierarchical truncated Gaussian mixture prior, which has the potential to encourage a low PAPR signal with most of its samples concentrated on the boundaries. A variational expectation-maximization (EM) strategy is developed to obtain estimates of the hyperparameters associated with the prior model, along with the signal. In addition, the generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results show our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where the base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain for outdoor wireless communications. Hybrid analog and digital beamforming structures are employed by both the BS and the MS due to hardware constraints. We propose a layered pilot transmission scheme and a CANDECOMP/PARAFAC (CP) decomposition-based method for joint estimation of the channels from multiple users (i.e., MSs) to the BS. The proposed method exploits the intrinsic low-rank structure of the multiway data collected from multiple modes, where the low-rank structure is a result of the sparse scattering nature of the mmWave channel. The uniqueness of the CP decomposition is studied, and the sufficient conditions for essential uniqueness are obtained. The conditions shed light on the design of the beamforming matrix, the combining matrix, and the pilot sequences, and meanwhile provide general guidelines for choosing system parameters. Our analysis reveals that our proposed method can achieve a substantial training overhead reduction by leveraging the low-rank structure of the received signal. Simulation results show that the proposed method presents a clear advantage over a compressed sensing-based method in terms of both estimation accuracy and computational complexity.

We investigate the secrecy capacity of Gaussian wiretap channel with a source, an external helper, an eavesdropper and a single-antenna legitimate receiver where the former three terminals are equipped with Na, Njand Neantennas, respectively. Generally, an analytical form of the secrecy capacity in this scenario is difficult to obtain. Instead, we recast the original nonconvex secrecy rate maximization (SRM) problem into a sequence of convex optimization problems. In doing so, the secrecy capacity can be obtained using a combination of convex optimization and a one-dimensional search. On the other hand, to gain more insight into how the secrecy capacity behaves, we study the secure degrees of freedom (s.d.o.f.) and quantify its connection with system parameters, where our result proves that the s.d.o.f. equal to 1 can be achieved if and only if Nea+Nj-1. As a by-product, we give a suboptimal but closed-form solution to the original SRM problem for the scenario where Nea+Nj-1.Numerical results are presented to validate the theoretical findings and illustrate the efficacy of the proposed schemes.

In this paper, we study the problem of sparse signal recovery when partial but partly erroneous prior knowledge of the signal's support is available. Based on the conventional sparse Bayesian learning framework, we propose an improved hierarchical prior model. The proposed modeling constitutes a three-layer hierarchical form. The first two layers, similar to the conventional sparse Bayesian learning, place a Gaussian-inverse-Gamma prior on the signal, while the third layer is newly added, with a prior placed on the parameters {bi}, where {bi} are parameters characterizing the sparsity-controlling hyperparameters {αi}. Such a modeling enables to automatically learn the true support from partly erroneous information through learning the values of the parameters {bi}. A variational Bayesian inference algorithm is developed based on the proposed prior model. Numerical results are provided to illustrate the performance of the proposed algorithm.

In this paper, we consider a massive MIMO wiretap system where the transmitter, the receiver and the eavesdropper are equipped with a large number of antennas. Being different from the previous works using asymptotic random matrix theory, our analysis relies on the concentration measure of non-asymptotic random matrix theory which allows us to obtain tight bounds for secrecy capacity of massive MIMO system with finite antenna number. The analytical and simulation results reveal the following, in the massive MIMO system employing equal power allocation at each transmit antenna: 1) the secrecy capacity falls within a bounds with a probability growing exponentially with the number of transmit antenna, while the ergodic secrecy capacity falls within a deterministic bounds; 2) the gap between the upper and the lower bound on secrecy rate is proportional to the square root of the SNR at legitimate receiver and the SNR at eavesdropper, respectively; 3) when the entry of legitimate channel matrix and eavesdropping channel matrix satisfies Gaussian distribution, the gap between the upper and the lower bound on secrecy rate is a linear reciprocal function of the number of transmit antennas.

We study the transmit design at relay nodes for secure amplify-and-forward (AF) networks. Two joint cooperative relaying and jamming schemes, namely Secrecy Rate Maximization Scheme and Null-Space Jamming Scheme, are proposed. In the first scheme, optimal relaying weight vector and optimal covariance matrix associated with the artificial noise (AN) are obtained, which involves doing a one-dimensional search and solving a sequence of semidefinite programs(SDPs). In the second scheme, which is suboptimal but computationally much cheaper, AN is designed to decrease the rate at the eavesdropper while the relaying weight vector is determined to increase the rate at the destination. In addition, Power Allocation based on secrecy rate maximization provides a balance between these two goals. Numerical results show that the proposed Secrecy Rate Maximization Scheme outperforms the existing cooperative relaying without jamming scheme. Especially, when the power is large enough, the secrecy rate achieved by the proposed schemes approaches the maximal achievable rate for the no-eavesdropper case.

We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

We consider a physical layer security problem where there is a source, an external helper, a legitimate receiver, and an eavesdropper, each equipped with one antenna. We assume that an additional out-of-band link from the source to the helper is available to improve the transmission security rate. A two-stage cooperative scheme is proposed. The proposed scheme consists of an information sharing stage and a cooperative transmission stage. Specifically, in the information sharing stage the source informs the helper of the signal to be transmitted, while in the cooperative transmission stage the source and the helper cooperate to transmit the signal to the legitimate receiver. Under this framework, we determine the optimal weights associated with this scheme and examine the secrecy capacity of the helper-assisted wiretap channel. The optimal weight design problem is generally nonconvex. To deal with this issue, an algorithm involving a one-dimensional search is developed. On the other hand, an analytical lower bound on the secrecy capacity is derived. Based on this lower bound, we further analyze the sufficient and necessary condition to ensure a positive secrecy capacity and derive the maximal achievable secure degrees of freedom, which are shown to be exactly the same as those of the multi-input single-output (MISO) wiretap channel.

We consider the problem of sparse signal recovery from one-bit measurements. Due to the noise present in the acquisition and transmission process, some quantized bits may be flipped to their opposite states. These bit-flip errors, also referred to as the sign-flip errors, may result in severe performance degradation. To address this issue, we introduce a robust Bayesian compressed sensing framework to account for sign flip errors. Specifically, sign-flip errors are considered as a result of a sparse noise-corrupted model in which original (unquantized) observations are corrupted by sparse (impulse) noise. A Gaussian-inverse Gamma hierarchical prior is assigned to the noise vector to promote sparsity. Based on the modified hierarchical model, we develop a variational expectation-maximization (EM) algorithm to identify the sign-flip errors and recover the sparse signal simultaneously. Numerical results are provided to illustrate the effectiveness and superiority of the proposed method.

In this letter, we introduce a new sparsity-promoting prior, namely, the “normal product” prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly different variances. Like other sparsity-encouraging distributions such as the Student’s$t$-distribution, the normal product distribution has a sharp peak at the origin, which makes it a suitable prior to encourage sparse solutions. A two-stage normal product-based hierarchical model is proposed. We resort to the variational Bayesian (VB) method to perform the inference. Simulations are conducted to illustrate the effectiveness of our proposed algorithm as compared with other state-of-the-art compressed sensing algorithms.

We consider the problem of distributed detection of a mean parameter corrupted by Gaussian noise in wireless sensor networks, where a large number of sensor nodes jointly detect the presence of a weak unknown signal. To circumvent power/bandwidth constraints, a multilevel quantizer is employed in each sensor to quantize the original observation. The quantized data are transmitted through binary symmetric channels to a fusion center where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. The asymptotic performance analysis of the multibit GLRT detector is provided, showing that the detection probability is monotonically increasing with respect to the Fisher information (FI) of the unknown signal parameter. We propose a quantizer design approach by maximizing the FI with respect to the quantization thresholds. Since the FI is a nonlinear and nonconvex function of the quantization thresholds, we employ the particle swarm optimization algorithm for FI maximization. Numerical results demonstrate that with 2- or 3-bit quantization, the GLRT detector can provide detection performance very close to that of the unquantized GLRT detector, which uses the original observations without quantization.

Hadamard ratio test is a well-known approach to robust signal detection in multivariate analysis. Recently, it has been exploited for robust spectrum sensing in cognitive radio, but its detection performance is not yet completely analyzed. This work is devoted to accurate detection performance analysis of the Hadamard ratio method for robust spectrum sensing. By computing the first and second exact negative moments for the signal-presence hypothesis along with employing the Beta distribution approximation, we derive accurate analytic formulae for detection probability. This enables us to theoretically evaluate the detection behavior of the Hadamard ratio test. Numerical results are presented to validate our theoretical findings.

This paper studies robust transmit design to maximize the worst-case secrecy rate in AF networks under both total and individual relay power constraints. Channel state information (CSI) in the network is assumed to be perfectly known except for that associated with the eavesdroppers whose imperfection is modeled as deterministic bounded errors. To use the power at the relay nodes more efficiently, a joint cooperative relaying and jamming scheme is considered. Through some matrix manipulations, we recast the original nonconvex optimization problem as a sequence of semidefinite programs (SDPs), which enables us to obtain the optimal relay weights and the optimal covariance matrix of the jamming signal. Numerical results are presented to show the efficacy of the proposed scheme.

In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. A pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm.

In many practical applications such as direction-of- arrival (DOA) estimation and line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing to such applications, the continuous parameter space has to be discretized to a finite set of grid points. Discretization, however, incurs errors and leads to deteriorated recovery performance. To address this issue, we propose an iterative reweighted method which jointly estimates the unknown parameters and the sparse signals. Specifically, the proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given objective function, which results in a gradual and interweaved iterative process to refine the unknown parameters and the sparse signal. Numerical results show that the algorithm provides superior performance in resolving closely-spaced frequency components.

We study the secrecy capacity of Gaussian wiretap channel aided by an external jammer/helper. Both the transmitter and the intended receiver are equipped with a single antenna, while the eavesdropper and the jammer are equipped with$M$and$N$antennas, respectively. Generally, an analytical form of the secrecy capacity in this scenario is difficult to obtain. Instead, we consider a null-space jamming scheme which totally nulls out the jamming signal at the legitimate receiver, and derive lower and upper bounds on its maximal achievable secrecy rate${R_N}$. The relationship between the average secrecy capacity${\bar C_N}$of Gaussian wiretap channel and the average secrecy rate${\bar R_N}$achieved by the null-space jamming scheme is investigated, and we prove that${\bar R_N} \leq {\bar C_N} \leq {\bar R_{N + 1}}$. Based on this inequality and the derived lower and upper bounds on${R_N}$, the upper and lower bounds on the average secrecy capacity of Gaussian wiretap channel aided by an external jammer can be obtained, where our result shows that when$N > M$, the average secrecy capacity increases linearly with the total transmit power; while when$N \leq M - 1$, there exists a performance ceiling on it.

In this paper, we consider a decentralized detection problem in which a number of sensor nodes collaborate to detect the presence of an unknown deterministic signal. Due to stringent power/bandwidth constraints, each sensor quantizes its local observation into one bit of information. The binary data are then sent to the fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to make a global decision. In this context, we study one-bit quantizer design and analyze the asymptotic performance of the one-bit GLRT detector for cases where the quantized data are sent to the FC via perfect or imperfect channels. Simulation results are carried out to corroborate our theoretical analysis and to illustrate the performance of the proposed scheme.

This paper considers the problem of reconstructing sparse or compressible signals from one-bit quantized measurements. We study a new method that uses a log-sum penalty function, also referred to as the Gaussian entropy, for sparse signal recovery. Additionally, in the proposed method, the sigmoid function is introduced to quantify the consistency between the measured one-bit quantized data and the reconstructed signal. A fast iterative algorithm is developed by iteratively minimizing a convex surrogate function that bounds the original objective function. This leads to an iterative reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Connections between the proposed algorithm and other existing methods are discussed. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

We consider the problem of interference alignment (IA) for the K-user constant multiple-input multiple-output interference channel (K-user MIMO IFC) over correlated channels with imperfect channel state information (CSI). Recent performance evaluations show that most of the existing IA algorithms suffer serious sum rate degradations when the available CSI is imperfect. To deal with this issue, an uplink-downlink (UL-DL) Average-Mean-Square-Error(AMSE) duality is firstly established for the K-user MIMO IFC. Based on this duality, a robust IA algorithm is developed. Numerical results show that the proposed algorithm not only achieves better sum rate performance than other existing algorithms, but can also accommodate to the case when the perfect CSI is not available.

In this letter, we consider a decentralized detection problem in which a number of sensor nodes collaborate to detect the presence of an unknown deterministic signal. Due to stringent power/bandwidth constraints, each sensor quantizes its local observation into one bit of information. The binary data are then sent to the fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to make a global decision. In this context, we study one-bit quantizer design and analyze the asymptotic performance of the one-bit GLRT detector for cases where the quantized data are sent to the FC via perfect or imperfect channels. Simulation results are carried out to corroborate our theoretical analysis and to illustrate the performance of the proposed scheme.

The fact that fewer measurements are needed by log-sum minimization for sparse signal recovery than the ℓ1-minimization has been observed by extensive experiments. Nevertheless, such a benefit brought by the use of the log-sum penalty function has not been rigorously proved. This paper provides a theoretical justification for adopting the log-sum as an alternative sparsity-encouraging function. We prove that minimizing the log-sum penalty function subject to Az = y is able to yield the exact solution, provided that a certain condition is satisfied. Specifically, our analysis suggests that, for a properly chosen regularization parameter, exact reconstruction can be attained when the restricted isometry constant δ3Kis smaller than one, which presents a less restrictive isometry condition than that required by the conventional ℓ1-type methods.

We consider the problem of transmitting multiple spatially distributed correlated sources to a common destination (e.g. a fusion center or an access point) in wireless sensor networks (WSNs). The correlated data from multiple sensors are jointly transmitted to the destination via orthogonal channels. We assume that the channel between each sensor and the receiver is multiple-input multiple-output (MIMO), with each sensor and the receiver equipped with multiple transmit/receive antennas. In this framework, we study the problem of joint linear precoder design for all sensors by assuming the knowledge of the instantaneous channel state information (CSI), aiming at maximizing the mutual information between the sources and the received signals at the destination. We propose a Gauss-Seidel iterative approach which successively optimizes the precoding matrix associated with each sensor, while fixing the other precoding matrices. Numerical results show that the proposed algorithm that takes into account the spatial correlation across sensors can achieve higher capacity than conventional methods that neglect the spatial correlation.

We study the problem of recovering the sparsity pattern of block-sparse signals from noise-corrupted measurements. A simple, efficient recovery method, namely, a block-version of the orthogonal matching pursuit (OMP) method, is considered in this paper and its behavior for recovering the block-sparsity pattern is analyzed. We provide sufficient conditions under which the block-version of the OMP can successfully recover the block-sparse representations in the presence of noise. Our analysis reveals that exploiting block-sparsity can improve the recovery ability and lead to a guaranteed recovery for a higher sparsity level. Numerical results are presented to corroborate our theoretical claim.

In this letter, we consider the detection of a multichannel signal with an unknown amplitude in colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, the covariance mismatch is caused by a target-induced subspace interference that is present only under the alternative hypothesis. According to the signal model, we propose a detector involving the following steps. The observation is first projected to the orthogonal complement of the signal to be detected, followed by a second projection to the interference subspace. Then, the energy of the doubly projected signal (residual) is computed. If the residual energy is small, the proposed detector reduces to the standard matched filter (MF), which ignores the subspace interference; otherwise, a modified test statistic is employed for additional interference cancellation. Simulation results are presented to demonstrate the effectiveness of the proposed detector.

To address the orphan problem caused by the limitation on the number of child nodes of a router in ZigBee networks, we propose an on-demand scalable address assignment algorithm based on segmentation of address spaces. Through segmenting the 16-bit address space of ZigBee networks according to the maximum address predefined by the distributed address assignment mechanism (DAAM), our algorithm enables a router to use the addresses in new space segments if it has no sufficient addresses to accommodate child nodes. In addition, we improve the tree routing protocol (TR) to adapt to extended addresses. Performance analysis and numerical results show that our proposed algorithm outperforms DAAM and one of its improvements in terms of the success rate of address assignment, communication overhead, and the average time spent to assign an address.

We consider the problem of distributed estimation of a vector parameter in wireless sensor networks (WSNs). Due to stringent power and bandwidth constraints, vector quantization is performed at each sensor to convert its local noisy vector observation into one bit of information. The one bit quantized data is then sent to the fusion center (FC), where a final estimate of the vector parameter is formed. The vector quantization problem is studied in such a distributed estimation context. Specifically, our study focuses on a class of hyperplane-based vector quantizers which linearly convert the observation vector into a scalar by using a compression vector and then carry out a scalar quantization. Under the framework of the Cramér-Rao bound (CRB) analysis, we study the choice of the quantization thresholds and the design of the compression vectors.

We consider the problem of distributed estimation in a power constrained collaborative wireless sensor network (WSN), where the network is divided into a set of sensor clusters, with collaboration allowed among sensors within the same cluster but not across clusters. Specifically, each cluster forms one or multiple local messages via sensor collaboration (in particular, linear operation is considered) and transmits the messages over noisy channels to a fusion center (FC). The final estimate is constructed at the FC based on the noisy data received from all clusters. In this collaborative setup, we study the following fundamental problems. Given a total transmit power constraint, shall we transmit the raw data or some low-dimensional local messages for each cluster? What is the optimal collaboration scheme for each cluster? How to optimally allocate the power among different clusters? These questions are addressed in this paper.

We consider the problem of distributed estimation of a Gauss-Markov random field using a wireless sensor network (WSN), where due to the stringent power and communication constraints, each sensor has to quantize its data before transmission. In this case, the convergence of conventional iterative matrix-splitting algorithms is hindered by the quantization errors. To address this issue, we propose a one-bit adaptive quantization approach which leads to decaying quantization errors. Numerical results show that even with one bit quantization, the proposed approach achieves a superior mean square deviation performance (with respect to the global linear minimum mean-square error estimate) within a moderate number of iterations.

The problem of distributed average consensus with quantized data is considered in this paper. We firstly propose a simple modification to the classical consensus protocol. Under a condition that the quantization noise variance converges to zero, the proposed protocol achieves a consensus in a mean squared sense and the consensus value is equal to the average of the initial state. Based on this result, we develop an adaptive quantization scheme which can adaptively adjust its quantization threshold and step-size by learning from previous runs, in a way such that the quantization noise variance at each sensor decreases to zero. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

This paper considers distributed estimation of a vector parameter in the presence of zero-mean additive multivariate Gaussian noise in wireless sensor networks. Due to stringent power and bandwidth constraints, vector quantization is performed at each sensor to convert its local noisy vector observation into one bit of information, which is then forwarded to a fusion center where a final estimate of the vector parameter is obtained. Within such a context, this paper focuses on a class of hyperplane-based vector quantizers which linearly convert the observation vector into a scalar by using a compression vector and then carry out a scalar quantization. It is shown that the key of the vector quantization design is to find a compression vector for each sensor. Under the framework of the Cramer-Rao bound (CRB) analysis, the compression vector design problem is formulated as an optimization problem that minimizes the trace of the CRB matrix. Such an optimization problem is extensively studied. In particular, an efficient iterative algorithm is developed for the general case, along with optimal and near-optimal solutions for some specific but important noise scenarios. Performance analysis and simulation results are carried out to illustrate the effectiveness of the proposed scheme.

We consider the problem of distributed estimation in a power constrained collaborative wireless sensor network (WSN), where the network is divided into a set of sensor clusters, with collaboration allowed among sensors within the same cluster but not across clusters. Specifically, each cluster forms one or multiple local messages via sensor collaboration (in particular, linear operation is considered) and transmits the messages over noisy channels to a fusion center (FC). The final estimate is constructed at the FC based on the noisy data received from all clusters. In this collaborative setup, we study the following fundamental problems. Given a total transmit power constraint, shall we transmit the raw data or some low-dimensional local messages for each cluster? What is the optimal collaboration scheme for each cluster? How to optimally allocate the power among different clusters? These questions are addressed in this paper. We will show that the optimum collaboration strategy is to compress the data into one local message which, depending on the channel characteristics, is transmitted using one or multiple available channels to the FC. The optimal power allocation among the clusters is also investigated, which yields a water- filling type of scheme.

We consider distributed estimation of a random vector parameter by a wireless sensor network (WSN). To meet stringent power and bandwidth budgets in WSN, local data compression is performed at each sensor to reduce the number of messages sent to a fusion center (FC). Under the constraint of a given total number of messages, our problem is to jointly determine the number of messages sent by each senor (a.k.a. dimension assignment) and design the corresponding compression matrix. The problem is formulated as a constrained optimization problem that minimizes the estimation mean-square error (MSE) at the FC. We analyze the problem using a subspace projection technique, which yields an efficient iterative solution. Numerical results are presented to illustrate the effectiveness of the proposed algorithm.

We consider the problem of distributed parameter estimation in wireless sensor networks (WSNs), where due to bandwidth/power constraints, each sensor quantizes its local observation into one bit of information that is sent to a fusion center (FC) to form a global estimate. Conventional fixed quantization (FQ) approaches, which utilize a fixed threshold for all sensors, incurs an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To overcome this difficulty, we propose a distributed adaptive quantization (AQ) approach, where, under the condition that sensors successively broadcast their quantized data, each sensor adaptively adjusts its quantization threshold using prior transmissions from other sensors. Specifically, our strategy here is to let each sensor choose its quantization threshold as the maximum likelihood (ML) estimate of the unknown parameter based on the quantized data sent from other sensors. The Cramér-Rao bound (CRB) analysis shows that our proposed one-bit AQ approach asymptotically attains an estimation variance that is only π/2 times that of the clairvoyant sample-mean estimator using unquantized observations.

We consider the problem of multitask learning (MTL), in which we simultaneously learn classifiers for multiple data sets (tasks), with sharing of intertask data as appropriate. We introduce a set of relevance parameters that control the degree to which data from other tasks are used in estimating the current task's classifier parameters. The set of relevance parameters are learned by maximizing their posterior probability, yielding an expectation-maximization (EM) algorithm. We illustrate the effectiveness of our approach through experimental results on a practical data set.

We consider distributed estimation of a random vector parameter by a wireless sensor network (WSN). To meet stringent power and bandwidth budgets in WSN, local data compression is performed at each sensor to reduce the number of messages sent to a fusion center (FC). Under the constraint of a given total number of messages, our problem is to jointly determine the number of messages sent by each senor (a.k.a. dimension assignment) and design the corresponding compression matrix. The problem is formulated as a constrained optimization problem that minimizes the estimation mean-square error (MSE) at the FC. We analyze the problem using a subspace projection technique, which yields an efficient iterative solution. Numerical results are presented to illustrate the effectiveness of the proposed algorithm.

We consider the problem of blind separation of discrete sources with finite alphabets. More specifically, multiple-amplitude-shift-keying (M-ASK) alphabet and complex quadrature amplitude modulation (QAM) alphabet are studied. The proposed separation method exploits the geometry of the received data constellation. The method relies on a finite-step noniterative algorithm, and therefore it is free from any convergence problem. Numerical simulation results are included to illustrate the performance of the proposed algorithm.

In this paper, the problem of distributed parameter estimation in a wireless sensor network is considered, where due to bandwidth constraint, each sensor node sends only one bit of information per sample to a fusion center. We propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. The maximum likelihood estimator (MLE) and the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme are derived. Numerical results depicting the performance and advantages of our approach over a fixed quantization scheme are presented.

Approximate formulations for the 3-dB beamwidth are derived in this letter to measure the spatial resolution of the broadband nonredundant array (NRA) and minimum redundancy array (MRA), which assume the ideal continuous-time, infinite-length filters with the frequency responses obtained by the linearly constrained minimum variance (LCMV) optimization. By these formulations, the beamwidths of NRA and MRA are compared with that of the uniform linear array (ULA). Moreover, the accuracy of the derived formulations is assessed by numerical studies.

In this letter, the problem of distributed parameter estimation in a wireless sensor network is considered, where due to bandwidth constraint, each sensor node sends only one bit of information to a fusion center. We propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. The maximum likelihood estimator (MLE) and the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme are derived. Numerical results depicting the performance and advantages of our approach over a fixed quantization scheme are presented.

We propose a nonredundant linear precoder for MIMO-OFDM which enables blind channel estimation. Due to the structure introduced by the precoding matrix, the channel can be estimated based on general SVD. The identifiability of the proposed algorithm is guaranteed even when the channel matrices share common zeros at subcarrier frequencies. Computer experiments show that the performance of our proposed algorithm compares favorably to the training based LS algorithm

In this paper, we consider the problem of blind multiple-input multiple-output (MIMO) finite impulse response (FIR) channel identification driven by spatially uncorrelated and temporally white input signals. A method that can entirely identify the MIMO channel based only on the second-order statistics (SOS) of the observed data is proposed. The complete identification of the convolutive mixture is accomplished by exploiting the diversity of the channel orders. The uniqueness of the proposed solution is proved. Numerical simulation results are presented to illustrate the performance of the proposed algorithm.

In this paper, we present a closed-form solution for blind multiple-input multiple-output (MIMO) finite impulse response (FIR) channel estimation driven by colored sources whose second-order statistics (SOS) are assumed to be known a priori. The proof for the uniqueness of the system solution is provided. Numerical simulation results are presented to illustrate the performance of the proposed algorithm.

We present a closed-form solution for blind single-input multiple-output finite impulse response channel estimation driven by colored sources. The second-order statistics of the input source are known a priori. The uniqueness of the system solution is proved by exploiting the derived property of companion matrices that are constructed from the inherent structural relationship between the source autocorrelation matrices. Numerical simulation results are presented to illustrate the performance of the proposed algorithm.

In this paper, we developed a subspace projection method for blind MIMO FIR identification, based on fourth order statistics of the output signals. The proposed method employs a subspace projection technique to identify the channel. The method identifies MIMO channels up to a nonsingular matrix, and it does not require exact knowledge of each user's channel order. Simulation results are included to verify our theoretical claims.